What Is Artificial Intelligence

Artificial Intelligence is a term for simulated intelligence in machines. It has been defined in many ways, such as:

Theory and development of computer systems able to perform tasks that normally require human intelligence

A branch of computer science dealing with the simulation of intelligent behavior in computers

The capability of a machine to imitate human behavior

A field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem-solving, and pattern recognition

History

John McCarthy first coined the term in 1956 when he invited a group of researchers from a variety of disciplines including language simulation, neuron nets, complexity theory and more to a summer workshop. His goal was to clarify and develop concepts around “thinking machines” with the bases that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

Important Events Regarding AI

From 1938 to 1946 - science fiction familiarized the general public with the concept of artificially intelligent robots
In literature :: Isaac Asimov’s “I, Robot” book - collection of nine stories in which Asimov plays with the logical possibility of human like robots (influenced the screenplay of the Will Smith Movie from 2004)
In film :: We saw an interpretation of a humanoid in the heartless tin man in one of the greatest films in history - “The Wonderful Wizard of Oz” (1939)
This led to a cultural assimilation of AU amongst philosophers and mathematicians alike.
In a paper published in 1950, “Computer Machinery and Intelligence,” the British polymath, Alan Turing, explored the mathematical possibility of AI, suggesting that machines could possibly solve problems and make decisions by mimicking human reasoning.
This catalyzed a discussion amongst the highly educated groups in our society.
And the term “Artificial Intelligence” came to fruition at the DSRPAI
Over time and as computers got more powerful and technology got cheaper to develop, AI began to flourish. Early demonstrations of this technology include:
Newell & Simon’s General Problem Solver (1959) - universal problem solver program that used well-formed formulas to solve puzzles using a trail and error approach
Weizenbaum’s ELIZA - Early Natural Language processing computer program that simulated conversation, giving the illusion of understanding (it followed a script)
These events aided in convincing government agencies such as DARPA to fund AI research at several institutions.

AI technology grew exponentially with Feigenbaum’s “Expert Systems” that mimicked the decision making process of a human expert. This type of system was used in Japan’s “Fifth Generation Computer Project” with an investment of $400M for the implementation of logic programming.

By the 2000’s many of the goals of the DSRPAI were achieved and were manifested in IBM’s Deep Blue chess playing computer defeating Gary Kasparov, the world champion at the time.

Real Life Applications of AI

IBM Watson at the 2018 US Open Watson gives tennis players a competitive edge. In August of 2018, IBM announced that Watson will begin a partnership with tennis players and coaches a like to give them the ability to improve their strategy which allows them to better prepare for games. IBM’s solution, which is already working with the USTA players development performance team, provides a technology solution that will help coaches and player better analyze their performances. The AI will review hours of match footage and automatically identify and index key points and stats and provide detailed reports.This not only helps players look at their own individual game and where to improve but also give them the option to scout upcoming opponents and evaluate their game to find weak points. This saves the player development team a lot of time, as the process previously would take hours now takes Watson minutes to go through videos, create the analysis and provide highlights as well as evolve the fan experience with AI Highlights.Previously the media team were in charge of creating the highlight reels that we would see during the game changes and at the end of the set, now that responsibility is given to Watson, now providing real time highlights of the best shots and plays for around the US open for the day.

Alexa – Amazon It a smart speaker from Amazon that can be queried about the weather, news, play songs on demand and some versions of the model can make calls and the whole thing is run on AI. Alexa is the Ai that runs the device.

Ridesharing apps such as Uber and Lyft : Apps such as uber and lyft utilize AI in order to determine the following:

1.The price of your ride
2.Minimize the wait time once you hail a car
3.Optimally match you with other passengers to minimize detours

Spotify Uses AI and algorithms to generate personally tailored playlists to users based on previous listening habits. The Spotify AI looks at specific variables such as time of listening , genre, mood of the music and etc to generate and recommend songs and similar artists that we may like based on our habits. Quite cool.

Simple AI uses:

i. Smart Email categorization – this is what Gmail uses to organize your email in primary, social, promotion or your other types of email topic categories.
ii. Spam email - Gmail for example successfully filters 99.9% of spam.
iii. Plagiarism Checkers Tools that allow educational institutions to analyze students work’s in order to determine if they were plagiarized
iv.Voice to text such as Apple TV, iMessage, Siri, WhatsApp

Pro's & Con's

Pro's

Simple Task’s: For simple tasks that we think are trivial and mundane, are easily done using AI and allows for a more intricate process automation increasing productivity allowing human employees to focus on more creative and higher priority tasks

Faster decisions, Fast Action, Faster Results: As a result of the automated cognitive processes of AI, areas such as automated fraud detection, data summary, planning and organization events, data

Data Analysis & Prediction: Big data now a days results in petabytes of data which would take a human a very long time to sift through however for AI, they can process these large amounts of data in a fraction of the time while deriving insights, analysis and predictions based on the data. Providing important conclusions and analysis

Error Free processing:Human’s make errors and that is a human quality but computers don’t, the only mistakes they make is when they are incorrectly programmed, and will ensure that there are no mistakes or errors made when processing and analyzing data

Better Research outcomes: As a result of AI and the benefits Ai provides in terms of data analysis it will help provide better predictions and forecasting

Con's

Job Losses: AI will result in a lot of low skilled and low cost workers to lose their jobs. Although AI will create a lot of wealth, more than it will destroy but the issue is with the redistribution of wealth and power.

Redistribution of Power: AI will mean that a lot of wealth and power will be concentrated in the hands of a few

De-Humanizes Warfare: AI technology in warfare can kill humans without involving an actual human to pull the trigger

Judgment Calls: AI doesn’t have the ability to make judgment calls, Example of The Sydney shooting in 2014 when there was a shooting and hostage drama downtown. People began ringing up Uber to get out of the affected area, and because of the surge in demand in a concentrated area, Uber's algorithms fell back on the trusted economics of supply-and-demand and ride rates skyrocketed. A lot of our judgments today are based on information, emotions and the our environment which Ai doesn’t have the ability to do unless we program it.

Case Study 1 - AI In Government

For decades AI researchers looked at enabling computers to perform a wide range of tasks previously demonstrated by humans. With the progression of technology AI programs can now play games, recognize faces and speech, learn and make informed decision. AI already has an impact on people’s lives and work. With the advancements of AI, the public sector is seeking and finding applications to improve services. Cognitive technologies could eventually revolutionize every facet of government operations.
For example, the Department of Homeland Security’s Citizenship and Immigration and Services have created a virtual assistant, EMMA that can respond accurately to human language.
EMMA uses its intelligence simply, showing relevant answers to questions—almost a half-million questions per month at present.
Overtime, AI will result in massive changes in the public sector, transforming how government employees get work done. This brings a lot of advantages I will mention in the coming slides. But there are also some setbacks, which are that it’s likely to eliminate some jobs and lead to the redesign of countless others jobs but in exchange it would also create entirely new professions. In the near future; even with the heavy incorporation of AI, analysis suggest that large government job losses are unlikely.
Instead they have looked into the positive effects in the labor market that would result because of the use of AI.

How much savings can AI in government generate?

- Analysis found that millions of working hours each year (out of some 4.3 billion worked total) could be freed up today by automating tasks that computers already routinely do. At the low end of the spectrum, it is estimated, automation could save 96.7 million federal hours annually, with a potential savings of $3.3 billion; at the high end, this rises to 1.2 billion hours and a potential annual savings of $41.1 billion

Case 2 - Open AI

About Open AI
Open AI is a non-profit AI research company that is dedicated to discovering and enacting the path to safe and effective Artificial General Intelligence.AGI, simply put is a machines ability to be able to perform any intellectual task a human being can.For example, if you ask a robot with AGI to hammer a nail it wouldn’t need to be programmed, it would do it like a human does, through trial and error.The goal of Open AI’ is to build safe AGI and ensure that the benefits of AGI are shared and as widely and fairly distributed as much as possible. The company focuses on long term research as they believe that AI will resolve short term issues and provide short term impact where as AGI is a long term game. As a result they publish works at top machine learning conferences and publish open-source software tools in order accelerate progress. Furthermore, they release regular blog posts in order to share research with the online community to push forward progress in the area.

Open AI 5
As mentioned Earlier, AGI is Artificial General Intelligence is not like regular AI in that there is a programming component to the technology but that it learns just like a human through trial and error. Elon musk wanted to display the progress that OpenAI has made by creating bots to play in a game of Dota. Because the Game is so complex and has a consistenyly shifting landscape and game environment, it makes the game very challenging for human players and computers a like. And the Test was meant to reflect the complexity and uncertainty of the real world. Dota is a strategy based game that revolves heavily on strategic manipulation of a map, quick response time and learning over 30 heroes unique skills and items. In a best of three series, Open AI’s bot were so dominant that in the first game the dota pro’s didn’t take a single tower, and only managed to get one in the second game. When the Bots first started in May, there were losing to amateur players, by June the AI had learned to the point that it was able to beat some of the best players in the world learning at an apparent rate of 180 years per day. The required time for a human to become a pro – 12,000 Hours - So what can we learn from this ?

Case 3 - Amazon Web Services Subsidiary

Definition by Amazon: a field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.
AWS uses Machine Learning to solve pragmatic business problems and develops simple-to-use and powerful ML tools and services. They test these tools in the scale and mission critical environment of Amazon.com before they are exposed for every business to use.
Amazon has invested deeply in artificial intelligence for over 20 years. Machine Learning algorithms drive many of their internal systems: from the optimization of fulfillment center to Amazon.com’s recommendations engine. They have shared their learning and ML capabilities as fully managed services for developers and data scientists.